Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations699
Missing cells16
Missing cells (%)0.2%
Duplicate rows48
Duplicate rows (%)6.9%
Total size in memory54.7 KiB
Average record size in memory80.2 B

Variable types

Numeric9
Categorical1

Alerts

Dataset has 48 (6.9%) duplicate rowsDuplicates
Clump_thickness is highly overall correlated with Bare_nuclei and 7 other fieldsHigh correlation
Uniformity_of_cell_size is highly overall correlated with Bare_nuclei and 8 other fieldsHigh correlation
Uniformity_of_cell_shape is highly overall correlated with Bare_nuclei and 7 other fieldsHigh correlation
Marginal_adhesion is highly overall correlated with Bare_nuclei and 7 other fieldsHigh correlation
Single_epithelial_cell_size is highly overall correlated with Bare_nuclei and 7 other fieldsHigh correlation
Bare_nuclei is highly overall correlated with Bland_chromatin and 7 other fieldsHigh correlation
Bland_chromatin is highly overall correlated with Bare_nuclei and 7 other fieldsHigh correlation
Normal_nucleoli is highly overall correlated with Bare_nuclei and 8 other fieldsHigh correlation
Class is highly overall correlated with Bare_nuclei and 8 other fieldsHigh correlation
Mitoses is highly overall correlated with Class and 2 other fieldsHigh correlation
Bare_nuclei has 16 (2.3%) missing valuesMissing

Reproduction

Analysis started2024-07-17 15:36:42.850858
Analysis finished2024-07-17 15:36:49.550956
Duration6.7 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Clump_thickness
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4177396
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:49.611837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8157407
Coefficient of variation (CV)0.63737135
Kurtosis-0.62371541
Mean4.4177396
Median Absolute Deviation (MAD)2
Skewness0.59285853
Sum3088
Variance7.9283955
MonotonicityNot monotonic
2024-07-17T11:36:49.695511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 145
20.7%
5 130
18.6%
3 108
15.5%
4 80
11.4%
10 69
9.9%
2 50
 
7.2%
8 46
 
6.6%
6 34
 
4.9%
7 23
 
3.3%
9 14
 
2.0%
ValueCountFrequency (%)
1 145
20.7%
2 50
 
7.2%
3 108
15.5%
4 80
11.4%
5 130
18.6%
6 34
 
4.9%
7 23
 
3.3%
8 46
 
6.6%
9 14
 
2.0%
10 69
9.9%
ValueCountFrequency (%)
10 69
9.9%
9 14
 
2.0%
8 46
 
6.6%
7 23
 
3.3%
6 34
 
4.9%
5 130
18.6%
4 80
11.4%
3 108
15.5%
2 50
 
7.2%
1 145
20.7%

Uniformity_of_cell_size
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1344778
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:49.771978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0514591
Coefficient of variation (CV)0.97351434
Kurtosis0.098802885
Mean3.1344778
Median Absolute Deviation (MAD)0
Skewness1.2331366
Sum2191
Variance9.3114027
MonotonicityNot monotonic
2024-07-17T11:36:49.852790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 384
54.9%
10 67
 
9.6%
3 52
 
7.4%
2 45
 
6.4%
4 40
 
5.7%
5 30
 
4.3%
8 29
 
4.1%
6 27
 
3.9%
7 19
 
2.7%
9 6
 
0.9%
ValueCountFrequency (%)
1 384
54.9%
2 45
 
6.4%
3 52
 
7.4%
4 40
 
5.7%
5 30
 
4.3%
6 27
 
3.9%
7 19
 
2.7%
8 29
 
4.1%
9 6
 
0.9%
10 67
 
9.6%
ValueCountFrequency (%)
10 67
 
9.6%
9 6
 
0.9%
8 29
 
4.1%
7 19
 
2.7%
6 27
 
3.9%
5 30
 
4.3%
4 40
 
5.7%
3 52
 
7.4%
2 45
 
6.4%
1 384
54.9%

Uniformity_of_cell_shape
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2074392
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:49.945926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9719128
Coefficient of variation (CV)0.9265687
Kurtosis0.00701098
Mean3.2074392
Median Absolute Deviation (MAD)0
Skewness1.1618592
Sum2242
Variance8.8322655
MonotonicityNot monotonic
2024-07-17T11:36:50.030349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
10 58
 
8.3%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
10 58
 
8.3%
ValueCountFrequency (%)
10 58
 
8.3%
9 7
 
1.0%
8 28
 
4.0%
7 30
 
4.3%
6 30
 
4.3%
5 34
 
4.9%
4 44
 
6.3%
3 56
 
8.0%
2 59
 
8.4%
1 353
50.5%

Marginal_adhesion
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.806867
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:50.108101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8553792
Coefficient of variation (CV)1.0172834
Kurtosis0.98794707
Mean2.806867
Median Absolute Deviation (MAD)0
Skewness1.5244681
Sum1962
Variance8.1531906
MonotonicityNot monotonic
2024-07-17T11:36:50.176443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 407
58.2%
3 58
 
8.3%
2 58
 
8.3%
10 55
 
7.9%
4 33
 
4.7%
8 25
 
3.6%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
9 5
 
0.7%
ValueCountFrequency (%)
1 407
58.2%
2 58
 
8.3%
3 58
 
8.3%
4 33
 
4.7%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
8 25
 
3.6%
9 5
 
0.7%
10 55
 
7.9%
ValueCountFrequency (%)
10 55
 
7.9%
9 5
 
0.7%
8 25
 
3.6%
7 13
 
1.9%
6 22
 
3.1%
5 23
 
3.3%
4 33
 
4.7%
3 58
 
8.3%
2 58
 
8.3%
1 407
58.2%

Single_epithelial_cell_size
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2160229
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:50.258825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2142999
Coefficient of variation (CV)0.68852118
Kurtosis2.1690664
Mean3.2160229
Median Absolute Deviation (MAD)0
Skewness1.7121718
Sum2248
Variance4.903124
MonotonicityNot monotonic
2024-07-17T11:36:50.348845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
1 47
 
6.7%
6 41
 
5.9%
5 39
 
5.6%
10 31
 
4.4%
8 21
 
3.0%
7 12
 
1.7%
9 2
 
0.3%
ValueCountFrequency (%)
1 47
 
6.7%
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
5 39
 
5.6%
6 41
 
5.9%
7 12
 
1.7%
8 21
 
3.0%
9 2
 
0.3%
10 31
 
4.4%
ValueCountFrequency (%)
10 31
 
4.4%
9 2
 
0.3%
8 21
 
3.0%
7 12
 
1.7%
6 41
 
5.9%
5 39
 
5.6%
4 48
 
6.9%
3 72
 
10.3%
2 386
55.2%
1 47
 
6.7%

Bare_nuclei
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)1.5%
Missing16
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean3.5446559
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:50.429374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6438572
Coefficient of variation (CV)1.0279861
Kurtosis-0.79884414
Mean3.5446559
Median Absolute Deviation (MAD)0
Skewness0.99001565
Sum2421
Variance13.277695
MonotonicityNot monotonic
2024-07-17T11:36:50.504725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 402
57.5%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
9 9
 
1.3%
7 8
 
1.1%
6 4
 
0.6%
(Missing) 16
 
2.3%
ValueCountFrequency (%)
1 402
57.5%
2 30
 
4.3%
3 28
 
4.0%
4 19
 
2.7%
5 30
 
4.3%
6 4
 
0.6%
7 8
 
1.1%
8 21
 
3.0%
9 9
 
1.3%
10 132
 
18.9%
ValueCountFrequency (%)
10 132
 
18.9%
9 9
 
1.3%
8 21
 
3.0%
7 8
 
1.1%
6 4
 
0.6%
5 30
 
4.3%
4 19
 
2.7%
3 28
 
4.0%
2 30
 
4.3%
1 402
57.5%

Bland_chromatin
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4377682
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:50.582708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4383643
Coefficient of variation (CV)0.70928698
Kurtosis0.18462131
Mean3.4377682
Median Absolute Deviation (MAD)1
Skewness1.0999691
Sum2403
Variance5.9456202
MonotonicityNot monotonic
2024-07-17T11:36:50.660609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 166
23.7%
3 165
23.6%
1 152
21.7%
7 73
10.4%
4 40
 
5.7%
5 34
 
4.9%
8 28
 
4.0%
10 20
 
2.9%
9 11
 
1.6%
6 10
 
1.4%
ValueCountFrequency (%)
1 152
21.7%
2 166
23.7%
3 165
23.6%
4 40
 
5.7%
5 34
 
4.9%
6 10
 
1.4%
7 73
10.4%
8 28
 
4.0%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.0%
7 73
10.4%
6 10
 
1.4%
5 34
 
4.9%
4 40
 
5.7%
3 165
23.6%
2 166
23.7%
1 152
21.7%

Normal_nucleoli
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8669528
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:50.738276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0536339
Coefficient of variation (CV)1.0651148
Kurtosis0.47426868
Mean2.8669528
Median Absolute Deviation (MAD)0
Skewness1.4222613
Sum2004
Variance9.32468
MonotonicityNot monotonic
2024-07-17T11:36:50.817163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 443
63.4%
10 61
 
8.7%
3 44
 
6.3%
2 36
 
5.2%
8 24
 
3.4%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
7 16
 
2.3%
9 16
 
2.3%
ValueCountFrequency (%)
1 443
63.4%
2 36
 
5.2%
3 44
 
6.3%
4 18
 
2.6%
5 19
 
2.7%
6 22
 
3.1%
7 16
 
2.3%
8 24
 
3.4%
9 16
 
2.3%
10 61
 
8.7%
ValueCountFrequency (%)
10 61
 
8.7%
9 16
 
2.3%
8 24
 
3.4%
7 16
 
2.3%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
3 44
 
6.3%
2 36
 
5.2%
1 443
63.4%

Mitoses
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5894134
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2024-07-17T11:36:50.895103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7150779
Coefficient of variation (CV)1.0790634
Kurtosis12.657878
Mean1.5894134
Median Absolute Deviation (MAD)0
Skewness3.5606578
Sum1111
Variance2.9414923
MonotonicityNot monotonic
2024-07-17T11:36:50.973247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
10 14
 
2.0%
4 12
 
1.7%
7 9
 
1.3%
8 8
 
1.1%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
4 12
 
1.7%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.1%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.1%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.7%
3 33
 
4.7%
2 35
 
5.0%
1 579
82.8%

Class
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2
458 
4
241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Length

2024-07-17T11:36:51.069959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T11:36:51.149287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring characters

ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Interactions

2024-07-17T11:36:48.488822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.116336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.786047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.457883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.134044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.788665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.476215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.132045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.816930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.553410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.197499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.868801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.536352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.192406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.864098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.551095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.207323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.884132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.633690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.270041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.941590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.613869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.272968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.945487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.630327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.285753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.957937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.691938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.348564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.018223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.676150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.351999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.020732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.707530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.364119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.038310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.773110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.415598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.085804image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.754531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.429520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.102742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.773266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.442659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.117542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:49.020355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.488626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.164032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.837221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.488708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.176471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.852405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.504635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.195725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:49.082746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.571876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.241807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.915186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.571083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.256844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.913634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.587830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.255224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:49.165263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.645307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.317132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.973379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.645182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.319418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.988598image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.660701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.337811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:49.238767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:43.722373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:44.382764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.055879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:45.723284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:46.394920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.070924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:47.741577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-07-17T11:36:48.415088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-07-17T11:36:51.207490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass
Clump_thickness1.0000.6450.6550.4860.5220.5930.5580.5360.3500.716
Uniformity_of_cell_size0.6451.0000.9070.7060.7520.6920.7560.7230.4590.818
Uniformity_of_cell_shape0.6550.9071.0000.6830.7200.7140.7360.7190.4390.819
Marginal_adhesion0.4860.7060.6831.0000.6000.6710.6670.6030.4180.697
Single_epithelial_cell_size0.5220.7520.7200.6001.0000.5860.6160.6290.4790.683
Bare_nuclei0.5930.6920.7140.6710.5861.0000.6810.5840.3390.823
Bland_chromatin0.5580.7560.7360.6670.6160.6811.0000.6660.3440.757
Normal_nucleoli0.5360.7230.7190.6030.6290.5840.6661.0000.4280.712
Mitoses0.3500.4590.4390.4180.4790.3390.3440.4281.0000.423
Class0.7160.8180.8190.6970.6830.8230.7570.7120.4231.000
2024-07-17T11:36:51.333199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass
Clump_thickness1.0000.6660.6640.5420.5840.5910.5380.5700.4190.682
Uniformity_of_cell_size0.6661.0000.8920.7430.7870.7700.7190.7570.5090.855
Uniformity_of_cell_shape0.6640.8921.0000.7120.7590.7530.6920.7250.4730.836
Marginal_adhesion0.5420.7430.7121.0000.6680.6970.6250.6340.4470.728
Single_epithelial_cell_size0.5840.7870.7590.6681.0000.6950.6400.7060.4800.763
Bare_nuclei0.5910.7700.7530.6970.6951.0000.6790.6600.4740.835
Bland_chromatin0.5380.7190.6920.6250.6400.6791.0000.6620.3870.740
Normal_nucleoli0.5700.7570.7250.6340.7060.6600.6621.0000.5040.744
Mitoses0.4190.5090.4730.4470.4800.4740.3870.5041.0000.527
Class0.6820.8550.8360.7280.7630.8350.7400.7440.5271.000
2024-07-17T11:36:51.461198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass
Clump_thickness1.0000.5370.5340.4330.4700.4790.4180.4620.3530.593
Uniformity_of_cell_size0.5371.0000.8150.6400.6820.6580.5990.6630.4550.778
Uniformity_of_cell_shape0.5340.8151.0000.6050.6500.6410.5720.6280.4190.754
Marginal_adhesion0.4330.6400.6051.0000.5710.6090.5110.5470.4000.667
Single_epithelial_cell_size0.4700.6820.6500.5711.0000.5960.5220.6090.4310.695
Bare_nuclei0.4790.6580.6410.6090.5961.0000.5650.5690.4250.776
Bland_chromatin0.4180.5990.5720.5110.5220.5651.0000.5610.3330.654
Normal_nucleoli0.4620.6630.6280.5470.6090.5690.5611.0000.4540.688
Mitoses0.3530.4550.4190.4000.4310.4250.3330.4541.0000.509
Class0.5930.7780.7540.6670.6950.7760.6540.6880.5091.000
2024-07-17T11:36:51.597753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass
Clump_thickness1.0000.6490.6470.5430.5910.6050.5790.5730.3790.904
Uniformity_of_cell_size0.6491.0000.8690.7090.7430.7120.7390.7340.4560.981
Uniformity_of_cell_shape0.6470.8691.0000.6960.7090.6990.7130.7080.4180.976
Marginal_adhesion0.5430.7090.6961.0000.7100.6700.6900.6180.4170.904
Single_epithelial_cell_size0.5910.7430.7090.7101.0000.6840.6480.6770.4990.942
Bare_nuclei0.6050.7120.6990.6700.6841.0000.6640.6580.4020.967
Bland_chromatin0.5790.7390.7130.6900.6480.6641.0000.6870.3880.949
Normal_nucleoli0.5730.7340.7080.6180.6770.6580.6871.0000.4310.926
Mitoses0.3790.4560.4180.4170.4990.4020.3880.4311.0000.520
Class0.9040.9810.9760.9040.9420.9670.9490.9260.5201.000
2024-07-17T11:36:51.711725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Bare_nucleiBland_chromatinClassClump_thicknessMarginal_adhesionMitosesNormal_nucleoliSingle_epithelial_cell_sizeUniformity_of_cell_shapeUniformity_of_cell_size
Bare_nuclei1.0000.6790.8390.5910.6970.4740.6600.6950.7530.770
Bland_chromatin0.6791.0000.8040.5380.6250.3870.6620.6400.6920.719
Class0.8390.8041.0000.7380.7380.5190.7680.7910.8600.875
Clump_thickness0.5910.5380.7381.0000.5420.4190.5700.5840.6640.666
Marginal_adhesion0.6970.6250.7380.5421.0000.4470.6340.6680.7120.743
Mitoses0.4740.3870.5190.4190.4471.0000.5040.4800.4730.509
Normal_nucleoli0.6600.6620.7680.5700.6340.5041.0000.7060.7250.757
Single_epithelial_cell_size0.6950.6400.7910.5840.6680.4800.7061.0000.7590.787
Uniformity_of_cell_shape0.7530.6920.8600.6640.7120.4730.7250.7591.0000.892
Uniformity_of_cell_size0.7700.7190.8750.6660.7430.5090.7570.7870.8921.000

Missing values

2024-07-17T11:36:49.333545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-17T11:36:49.479295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass
0511121.03112
15445710.03212
2311122.03112
3688134.03712
4411321.03112
5810108710.09714
61111210.03112
7212121.03112
8211121.01152
9421121.02112
Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass
689111121.01182
690111321.01112
69151010545.04414
692311121.01112
693311121.02122
694311132.01112
695211121.01112
69651010373.081024
697486434.010614
698488545.010414

Duplicate rows

Most frequently occurring

Clump_thicknessUniformity_of_cell_sizeUniformity_of_cell_shapeMarginal_adhesionSingle_epithelial_cell_sizeBare_nucleiBland_chromatinNormal_nucleoliMitosesClass# duplicates
4111121.0111227
6111121.0311223
5111121.0211221
20311121.0211220
19311121.0111212
13211121.0111210
21311121.0311210
28411121.0111210
29411121.0211210
37511121.0211210